End-to-end Object detection: Though object detection has been extensively studied in the literature, we can only find a few end-to-end learning pipelines capable of simultaneous object localization and classification. Popular examples of such approaches are Faster R-CNN [1]}, R-FCN [2]}, SSD [3]} and YOLO [4]}. The contribution of these methods relies on object localization process. Methods like Faster R-CNN [1]}, R-FCN [2]} are based on Region Proposal Network (RPN) which provides bounding box proposals of possible objects and then classifying and fine tuning the box prediction in the later layers. In contrast, methods like SSD [3]} and YOLO [4]} draw bounding box and classify it in a single step. Unlike RPN; these methods predict bounding box offset of some pre-defined anchors instead of the box co-ordinates itself. The later methods are generally faster than the previous ones. However, RPN based methods are better in terms of accuracy. In current work, we prioritize accuracy over speed. Therefore, we build zero-shot object detection model based on the Faster RCNN.
